Paper Info
Title
Unsupervised Deep Features for Remote Sensing Image Matching via Discriminator Network.
Abstract
The advent of deep perceptual networks brought about a paradigm shift in machine vision and image perception. Image apprehension lately carried out by hand-crafted features in the latent space have been replaced by deep features acquired from supervised networks for improved understanding. However, such deep networks require strict supervision with a substantial amount of the labeled data for authentic training process. These methods perform poorly in domains lacking labeled data especially in case of remote sensing image retrieval. Resolving this, we propose an unsupervised encoder-decoder feature for remote sensing image matching (RSIM). Moreover, we replace the conventional distance metrics with a deep discriminator network to identify the similarity of the image pairs. To the best of our knowledge, discriminator network has never been used before for solving RSIM problem. Results have been validated with two publicly available benchmark remote sensing image datasets. The technique has also been investigated for content-based remote sensing image retrieval (CBRSIR); one of the widely used applications of RSIM. Results demonstrate that our technique supersedes the state-of-the-art methods used for unsupervised image matching with mean average precision (mAP) of 81%, and image retrieval with an overall improvement in mAP score of about 12%.
Year
Venue
Field
2018
arXiv: Computer Vision and Pattern Recognition
Discriminator,Pattern recognition,Machine vision,Image matching,Computer science,Remote sensing,Image retrieval,Artificial intelligence,Labeled data
DocType
Volume
Citations 
Journal
abs/1810.06470
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Mohbat Tharani101.35
Numan Khurshid221.72
Murtaza Taj325018.85